Overview

Dataset statistics

Number of variables31
Number of observations577262
Missing cells2779102
Missing cells (%)15.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory136.5 MiB
Average record size in memory248.0 B

Variable types

Numeric20
DateTime1
Categorical4
Text6

Alerts

CANCELLED is highly imbalanced (85.2%)Imbalance
DIVERTED is highly imbalanced (96.3%)Imbalance
DEP_TIME has 11603 (2.0%) missing valuesMissing
DEP_DELAY has 11607 (2.0%) missing valuesMissing
TAXI_OUT has 12137 (2.1%) missing valuesMissing
TAXI_IN has 12413 (2.2%) missing valuesMissing
ARR_TIME has 12413 (2.2%) missing valuesMissing
ARR_DELAY has 14458 (2.5%) missing valuesMissing
CANCELLATION_CODE has 565043 (97.9%) missing valuesMissing
AIR_TIME has 14458 (2.5%) missing valuesMissing
CARRIER_DELAY has 424994 (73.6%) missing valuesMissing
WEATHER_DELAY has 424994 (73.6%) missing valuesMissing
NAS_DELAY has 424994 (73.6%) missing valuesMissing
SECURITY_DELAY has 424994 (73.6%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 424994 (73.6%) missing valuesMissing
SECURITY_DELAY is highly skewed (γ1 = 40.74569553)Skewed
DEP_DELAY has 26175 (4.5%) zerosZeros
ARR_DELAY has 10607 (1.8%) zerosZeros
CARRIER_DELAY has 64488 (11.2%) zerosZeros
WEATHER_DELAY has 141926 (24.6%) zerosZeros
NAS_DELAY has 79641 (13.8%) zerosZeros
SECURITY_DELAY has 151369 (26.2%) zerosZeros
LATE_AIRCRAFT_DELAY has 68409 (11.9%) zerosZeros

Reproduction

Analysis started2024-03-30 06:08:01.581862
Analysis finished2024-03-30 06:10:58.286115
Duration2 minutes and 56.7 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.010432
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:10:58.393907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9376956
Coefficient of variation (CV)0.4831638
Kurtosis-1.1307111
Mean4.010432
Median Absolute Deviation (MAD)2
Skewness-0.036328573
Sum2315070
Variance3.7546641
MonotonicityIncreasing
2024-03-30T03:10:58.651030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 99853
17.3%
4 99601
17.3%
1 79769
13.8%
7 77241
13.4%
3 76217
13.2%
2 74798
13.0%
6 69783
12.1%
ValueCountFrequency (%)
1 79769
13.8%
2 74798
13.0%
3 76217
13.2%
4 99601
17.3%
5 99853
17.3%
6 69783
12.1%
7 77241
13.4%
ValueCountFrequency (%)
7 77241
13.4%
6 69783
12.1%
5 99853
17.3%
4 99601
17.3%
3 76217
13.2%
2 74798
13.0%
1 79769
13.8%
Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
Minimum2023-06-01 00:00:00
Maximum2023-06-30 00:00:00
2024-03-30T03:10:58.943899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:59.261076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
WN
119251 
DL
85929 
AA
80416 
UA
62395 
OO
56372 
Other values (10)
172899 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1154524
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 119251
20.7%
DL 85929
14.9%
AA 80416
13.9%
UA 62395
10.8%
OO 56372
9.8%
YX 23752
 
4.1%
B6 22795
 
3.9%
AS 21337
 
3.7%
NK 21155
 
3.7%
9E 17307
 
3.0%
Other values (5) 66553
11.5%

Length

2024-03-30T03:10:59.569538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 119251
20.7%
dl 85929
14.9%
aa 80416
13.9%
ua 62395
10.8%
oo 56372
9.8%
yx 23752
 
4.1%
b6 22795
 
3.9%
as 21337
 
3.7%
nk 21155
 
3.7%
9e 17307
 
3.0%
Other values (5) 66553
11.5%

Most occurring characters

ValueCountFrequency (%)
A 251396
21.8%
N 140406
12.2%
O 129869
11.2%
W 119251
10.3%
D 85929
 
7.4%
L 85929
 
7.4%
U 62395
 
5.4%
9 31244
 
2.7%
H 23957
 
2.1%
Y 23752
 
2.1%
Other values (11) 200396
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1154524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 251396
21.8%
N 140406
12.2%
O 129869
11.2%
W 119251
10.3%
D 85929
 
7.4%
L 85929
 
7.4%
U 62395
 
5.4%
9 31244
 
2.7%
H 23957
 
2.1%
Y 23752
 
2.1%
Other values (11) 200396
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1154524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 251396
21.8%
N 140406
12.2%
O 129869
11.2%
W 119251
10.3%
D 85929
 
7.4%
L 85929
 
7.4%
U 62395
 
5.4%
9 31244
 
2.7%
H 23957
 
2.1%
Y 23752
 
2.1%
Other values (11) 200396
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1154524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 251396
21.8%
N 140406
12.2%
O 129869
11.2%
W 119251
10.3%
D 85929
 
7.4%
L 85929
 
7.4%
U 62395
 
5.4%
9 31244
 
2.7%
H 23957
 
2.1%
Y 23752
 
2.1%
Other values (11) 200396
17.4%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5936
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2324.8165
Minimum1
Maximum8819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:00.066307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile279
Q11044
median2082
Q33361
95-th percentile5394
Maximum8819
Range8818
Interquartile range (IQR)2317

Descriptive statistics

Standard deviation1580.8162
Coefficient of variation (CV)0.67997464
Kurtosis-0.62942795
Mean2324.8165
Median Absolute Deviation (MAD)1117
Skewness0.58704669
Sum1.3420282 × 109
Variance2498979.9
MonotonicityNot monotonic
2024-03-30T03:11:00.451720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 341
 
0.1%
352 310
 
0.1%
1008 307
 
0.1%
1267 298
 
0.1%
2607 296
 
0.1%
555 292
 
0.1%
470 289
 
0.1%
777 289
 
0.1%
323 286
 
< 0.1%
1191 283
 
< 0.1%
Other values (5926) 574271
99.5%
ValueCountFrequency (%)
1 182
< 0.1%
2 176
< 0.1%
3 177
< 0.1%
4 187
< 0.1%
5 106
< 0.1%
6 88
< 0.1%
7 130
< 0.1%
8 125
< 0.1%
9 206
< 0.1%
10 185
< 0.1%
ValueCountFrequency (%)
8819 2
< 0.1%
8818 1
 
< 0.1%
8810 1
 
< 0.1%
8809 1
 
< 0.1%
8801 1
 
< 0.1%
8788 3
< 0.1%
8787 1
 
< 0.1%
8785 1
 
< 0.1%
8784 1
 
< 0.1%
8783 1
 
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12645.136
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:00.786257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1533.3962
Coefficient of variation (CV)0.12126371
Kurtosis-1.300608
Mean12645.136
Median Absolute Deviation (MAD)1591
Skewness0.11152313
Sum7.2995566 × 109
Variance2351303.8
MonotonicityNot monotonic
2024-03-30T03:11:01.141042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 28685
 
5.0%
11298 24996
 
4.3%
11292 24185
 
4.2%
13930 22073
 
3.8%
12892 16779
 
2.9%
11057 16674
 
2.9%
12889 15535
 
2.7%
14747 14679
 
2.5%
12953 13568
 
2.4%
14107 13355
 
2.3%
Other values (329) 386733
67.0%
ValueCountFrequency (%)
10135 374
 
0.1%
10136 116
 
< 0.1%
10140 1909
0.3%
10141 60
 
< 0.1%
10146 60
 
< 0.1%
10154 343
 
0.1%
10155 90
 
< 0.1%
10157 142
 
< 0.1%
10158 210
 
< 0.1%
10165 8
 
< 0.1%
ValueCountFrequency (%)
16869 140
 
< 0.1%
16218 145
 
< 0.1%
15991 60
 
< 0.1%
15919 1022
0.2%
15897 60
 
< 0.1%
15841 60
 
< 0.1%
15624 1034
0.2%
15607 60
 
< 0.1%
15582 52
 
< 0.1%
15569 52
 
< 0.1%

ORIGIN
Text

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:01.849892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1731786
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLT
2nd rowJFK
3rd rowBHM
4th rowLGA
5th rowBGR
ValueCountFrequency (%)
atl 28685
 
5.0%
dfw 24996
 
4.3%
den 24185
 
4.2%
ord 22073
 
3.8%
lax 16779
 
2.9%
clt 16674
 
2.9%
las 15535
 
2.7%
sea 14679
 
2.5%
lga 13568
 
2.4%
phx 13355
 
2.3%
Other values (329) 386733
67.0%
2024-03-30T03:11:02.885556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 198304
 
11.5%
L 158807
 
9.2%
S 148273
 
8.6%
D 137166
 
7.9%
T 91609
 
5.3%
C 88456
 
5.1%
O 88366
 
5.1%
M 77730
 
4.5%
F 72627
 
4.2%
W 68030
 
3.9%
Other values (16) 602418
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1731786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 198304
 
11.5%
L 158807
 
9.2%
S 148273
 
8.6%
D 137166
 
7.9%
T 91609
 
5.3%
C 88456
 
5.1%
O 88366
 
5.1%
M 77730
 
4.5%
F 72627
 
4.2%
W 68030
 
3.9%
Other values (16) 602418
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1731786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 198304
 
11.5%
L 158807
 
9.2%
S 148273
 
8.6%
D 137166
 
7.9%
T 91609
 
5.3%
C 88456
 
5.1%
O 88366
 
5.1%
M 77730
 
4.5%
F 72627
 
4.2%
W 68030
 
3.9%
Other values (16) 602418
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1731786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 198304
 
11.5%
L 158807
 
9.2%
S 148273
 
8.6%
D 137166
 
7.9%
T 91609
 
5.3%
C 88456
 
5.1%
O 88366
 
5.1%
M 77730
 
4.5%
F 72627
 
4.2%
W 68030
 
3.9%
Other values (16) 602418
34.8%
Distinct333
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:03.395961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.060674
Min length8

Characters and Unicode

Total characters7539431
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCharlotte, NC
2nd rowNew York, NY
3rd rowBirmingham, AL
4th rowNew York, NY
5th rowBangor, ME
ValueCountFrequency (%)
ca 62634
 
4.7%
tx 61128
 
4.5%
fl 48495
 
3.6%
ny 31097
 
2.3%
san 30883
 
2.3%
ga 30779
 
2.3%
il 30534
 
2.3%
chicago 29410
 
2.2%
atlanta 28685
 
2.1%
new 28578
 
2.1%
Other values (404) 962163
71.6%
2024-03-30T03:11:04.219803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
767124
 
10.2%
a 577908
 
7.7%
, 577262
 
7.7%
o 415793
 
5.5%
e 396008
 
5.3%
n 370884
 
4.9%
t 363593
 
4.8%
l 335909
 
4.5%
i 286281
 
3.8%
r 272170
 
3.6%
Other values (47) 3176499
42.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7539431
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
767124
 
10.2%
a 577908
 
7.7%
, 577262
 
7.7%
o 415793
 
5.5%
e 396008
 
5.3%
n 370884
 
4.9%
t 363593
 
4.8%
l 335909
 
4.5%
i 286281
 
3.8%
r 272170
 
3.6%
Other values (47) 3176499
42.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7539431
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
767124
 
10.2%
a 577908
 
7.7%
, 577262
 
7.7%
o 415793
 
5.5%
e 396008
 
5.3%
n 370884
 
4.9%
t 363593
 
4.8%
l 335909
 
4.5%
i 286281
 
3.8%
r 272170
 
3.6%
Other values (47) 3176499
42.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7539431
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
767124
 
10.2%
a 577908
 
7.7%
, 577262
 
7.7%
o 415793
 
5.5%
e 396008
 
5.3%
n 370884
 
4.9%
t 363593
 
4.8%
l 335909
 
4.5%
i 286281
 
3.8%
r 272170
 
3.6%
Other values (47) 3176499
42.1%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:04.632122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1746399
Min length4

Characters and Unicode

Total characters4718909
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth Carolina
2nd rowNew York
3rd rowAlabama
4th rowNew York
5th rowMaine
ValueCountFrequency (%)
california 62634
 
9.5%
texas 61128
 
9.2%
florida 48495
 
7.3%
new 45477
 
6.9%
york 31097
 
4.7%
georgia 30779
 
4.6%
illinois 30534
 
4.6%
carolina 30507
 
4.6%
colorado 26795
 
4.0%
north 26145
 
3.9%
Other values (51) 268554
40.6%
2024-03-30T03:11:05.281416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 634260
13.4%
i 529663
 
11.2%
o 450969
 
9.6%
n 350949
 
7.4%
r 338596
 
7.2%
e 289413
 
6.1%
s 271594
 
5.8%
l 261388
 
5.5%
C 121647
 
2.6%
t 114863
 
2.4%
Other values (37) 1355567
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4718909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 634260
13.4%
i 529663
 
11.2%
o 450969
 
9.6%
n 350949
 
7.4%
r 338596
 
7.2%
e 289413
 
6.1%
s 271594
 
5.8%
l 261388
 
5.5%
C 121647
 
2.6%
t 114863
 
2.4%
Other values (37) 1355567
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4718909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 634260
13.4%
i 529663
 
11.2%
o 450969
 
9.6%
n 350949
 
7.4%
r 338596
 
7.2%
e 289413
 
6.1%
s 271594
 
5.8%
l 261388
 
5.5%
C 121647
 
2.6%
t 114863
 
2.4%
Other values (37) 1355567
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4718909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 634260
13.4%
i 529663
 
11.2%
o 450969
 
9.6%
n 350949
 
7.4%
r 338596
 
7.2%
e 289413
 
6.1%
s 271594
 
5.8%
l 261388
 
5.5%
C 121647
 
2.6%
t 114863
 
2.4%
Other values (37) 1355567
28.7%

ORIGIN_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.464512
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:05.651202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median45
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.894742
Coefficient of variation (CV)0.49380306
Kurtosis-1.3066313
Mean54.464512
Median Absolute Deviation (MAD)23
Skewness-0.031710474
Sum31440293
Variance723.32717
MonotonicityNot monotonic
2024-03-30T03:11:05.960250image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 62634
 
10.9%
74 61128
 
10.6%
33 48495
 
8.4%
22 31097
 
5.4%
34 30779
 
5.3%
41 30534
 
5.3%
82 26795
 
4.6%
36 24829
 
4.3%
38 19876
 
3.4%
85 17229
 
3.0%
Other values (42) 223866
38.8%
ValueCountFrequency (%)
1 3793
 
0.7%
2 11333
2.0%
3 3374
 
0.6%
4 486
 
0.1%
5 119
 
< 0.1%
11 1711
 
0.3%
12 1687
 
0.3%
13 12570
2.2%
14 545
 
0.1%
15 1142
 
0.2%
ValueCountFrequency (%)
93 16886
 
2.9%
92 6743
 
1.2%
91 62634
10.9%
88 883
 
0.2%
87 9633
 
1.7%
86 2119
 
0.4%
85 17229
 
3.0%
84 2497
 
0.4%
83 2228
 
0.4%
82 26795
4.6%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12644.925
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:06.479450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1533.369
Coefficient of variation (CV)0.12126359
Kurtosis-1.3004438
Mean12644.925
Median Absolute Deviation (MAD)1591
Skewness0.11173399
Sum7.2994345 × 109
Variance2351220.5
MonotonicityNot monotonic
2024-03-30T03:11:06.815840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 28678
 
5.0%
11298 24985
 
4.3%
11292 24192
 
4.2%
13930 22082
 
3.8%
12892 16783
 
2.9%
11057 16658
 
2.9%
12889 15533
 
2.7%
14747 14669
 
2.5%
12953 13570
 
2.4%
14107 13346
 
2.3%
Other values (329) 386766
67.0%
ValueCountFrequency (%)
10135 374
 
0.1%
10136 116
 
< 0.1%
10140 1911
0.3%
10141 60
 
< 0.1%
10146 60
 
< 0.1%
10154 343
 
0.1%
10155 90
 
< 0.1%
10157 142
 
< 0.1%
10158 211
 
< 0.1%
10165 8
 
< 0.1%
ValueCountFrequency (%)
16869 140
 
< 0.1%
16218 146
 
< 0.1%
15991 60
 
< 0.1%
15919 1021
0.2%
15897 60
 
< 0.1%
15841 60
 
< 0.1%
15624 1034
0.2%
15607 60
 
< 0.1%
15582 52
 
< 0.1%
15569 52
 
< 0.1%

DEST
Text

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:07.477864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1731786
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowJFK
2nd rowCLT
3rd rowLGA
4th rowBHM
5th rowLGA
ValueCountFrequency (%)
atl 28678
 
5.0%
dfw 24985
 
4.3%
den 24192
 
4.2%
ord 22082
 
3.8%
lax 16783
 
2.9%
clt 16658
 
2.9%
las 15533
 
2.7%
sea 14669
 
2.5%
lga 13570
 
2.4%
phx 13346
 
2.3%
Other values (329) 386766
67.0%
2024-03-30T03:11:08.592270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 198312
 
11.5%
L 158784
 
9.2%
S 148266
 
8.6%
D 137160
 
7.9%
T 91581
 
5.3%
C 88446
 
5.1%
O 88364
 
5.1%
M 77734
 
4.5%
F 72608
 
4.2%
W 68033
 
3.9%
Other values (16) 602498
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1731786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 198312
 
11.5%
L 158784
 
9.2%
S 148266
 
8.6%
D 137160
 
7.9%
T 91581
 
5.3%
C 88446
 
5.1%
O 88364
 
5.1%
M 77734
 
4.5%
F 72608
 
4.2%
W 68033
 
3.9%
Other values (16) 602498
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1731786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 198312
 
11.5%
L 158784
 
9.2%
S 148266
 
8.6%
D 137160
 
7.9%
T 91581
 
5.3%
C 88446
 
5.1%
O 88364
 
5.1%
M 77734
 
4.5%
F 72608
 
4.2%
W 68033
 
3.9%
Other values (16) 602498
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1731786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 198312
 
11.5%
L 158784
 
9.2%
S 148266
 
8.6%
D 137160
 
7.9%
T 91581
 
5.3%
C 88446
 
5.1%
O 88364
 
5.1%
M 77734
 
4.5%
F 72608
 
4.2%
W 68033
 
3.9%
Other values (16) 602498
34.8%
Distinct333
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:09.201582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.06029
Min length8

Characters and Unicode

Total characters7539209
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNew York, NY
2nd rowCharlotte, NC
3rd rowNew York, NY
4th rowBirmingham, AL
5th rowNew York, NY
ValueCountFrequency (%)
ca 62626
 
4.7%
tx 61130
 
4.5%
fl 48471
 
3.6%
ny 31103
 
2.3%
san 30884
 
2.3%
ga 30773
 
2.3%
il 30547
 
2.3%
chicago 29423
 
2.2%
atlanta 28678
 
2.1%
new 28583
 
2.1%
Other values (404) 962161
71.6%
2024-03-30T03:11:10.134933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
767117
 
10.2%
a 577823
 
7.7%
, 577262
 
7.7%
o 415775
 
5.5%
e 396016
 
5.3%
n 370865
 
4.9%
t 363521
 
4.8%
l 335849
 
4.5%
i 286278
 
3.8%
r 272128
 
3.6%
Other values (47) 3176575
42.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7539209
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
767117
 
10.2%
a 577823
 
7.7%
, 577262
 
7.7%
o 415775
 
5.5%
e 396016
 
5.3%
n 370865
 
4.9%
t 363521
 
4.8%
l 335849
 
4.5%
i 286278
 
3.8%
r 272128
 
3.6%
Other values (47) 3176575
42.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7539209
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
767117
 
10.2%
a 577823
 
7.7%
, 577262
 
7.7%
o 415775
 
5.5%
e 396016
 
5.3%
n 370865
 
4.9%
t 363521
 
4.8%
l 335849
 
4.5%
i 286278
 
3.8%
r 272128
 
3.6%
Other values (47) 3176575
42.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7539209
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
767117
 
10.2%
a 577823
 
7.7%
, 577262
 
7.7%
o 415775
 
5.5%
e 396016
 
5.3%
n 370865
 
4.9%
t 363521
 
4.8%
l 335849
 
4.5%
i 286278
 
3.8%
r 272128
 
3.6%
Other values (47) 3176575
42.1%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:10.535469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1746607
Min length4

Characters and Unicode

Total characters4718921
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowNorth Carolina
3rd rowNew York
4th rowAlabama
5th rowNew York
ValueCountFrequency (%)
california 62626
 
9.5%
texas 61130
 
9.2%
florida 48471
 
7.3%
new 45498
 
6.9%
york 31103
 
4.7%
georgia 30773
 
4.6%
illinois 30547
 
4.6%
carolina 30506
 
4.6%
colorado 26808
 
4.0%
north 26137
 
3.9%
Other values (51) 268570
40.6%
2024-03-30T03:11:11.179784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 634211
13.4%
i 529591
 
11.2%
o 450982
 
9.6%
n 350904
 
7.4%
r 338560
 
7.2%
e 289489
 
6.1%
s 271638
 
5.8%
l 261390
 
5.5%
C 121650
 
2.6%
t 114867
 
2.4%
Other values (37) 1355639
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4718921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 634211
13.4%
i 529591
 
11.2%
o 450982
 
9.6%
n 350904
 
7.4%
r 338560
 
7.2%
e 289489
 
6.1%
s 271638
 
5.8%
l 261390
 
5.5%
C 121650
 
2.6%
t 114867
 
2.4%
Other values (37) 1355639
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4718921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 634211
13.4%
i 529591
 
11.2%
o 450982
 
9.6%
n 350904
 
7.4%
r 338560
 
7.2%
e 289489
 
6.1%
s 271638
 
5.8%
l 261390
 
5.5%
C 121650
 
2.6%
t 114867
 
2.4%
Other values (37) 1355639
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4718921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 634211
13.4%
i 529591
 
11.2%
o 450982
 
9.6%
n 350904
 
7.4%
r 338560
 
7.2%
e 289489
 
6.1%
s 271638
 
5.8%
l 261390
 
5.5%
C 121650
 
2.6%
t 114867
 
2.4%
Other values (37) 1355639
28.7%

DEST_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.463587
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:11.509520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median45
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.894312
Coefficient of variation (CV)0.49380355
Kurtosis-1.3065551
Mean54.463587
Median Absolute Deviation (MAD)23
Skewness-0.031741565
Sum31439759
Variance723.30403
MonotonicityNot monotonic
2024-03-30T03:11:11.788103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 62626
 
10.8%
74 61130
 
10.6%
33 48471
 
8.4%
22 31103
 
5.4%
34 30773
 
5.3%
41 30547
 
5.3%
82 26808
 
4.6%
36 24820
 
4.3%
38 19867
 
3.4%
85 17230
 
3.0%
Other values (42) 223887
38.8%
ValueCountFrequency (%)
1 3792
 
0.7%
2 11330
2.0%
3 3381
 
0.6%
4 486
 
0.1%
5 119
 
< 0.1%
11 1710
 
0.3%
12 1686
 
0.3%
13 12576
2.2%
14 546
 
0.1%
15 1139
 
0.2%
ValueCountFrequency (%)
93 16875
 
2.9%
92 6735
 
1.2%
91 62626
10.8%
88 888
 
0.2%
87 9632
 
1.7%
86 2121
 
0.4%
85 17230
 
3.0%
84 2502
 
0.4%
83 2230
 
0.4%
82 26808
4.6%

CRS_DEP_TIME
Real number (ℝ)

Distinct1242
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1339.4507
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:12.103432image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile600
Q1908
median1327
Q31750
95-th percentile2145
Maximum2359
Range2358
Interquartile range (IQR)842

Descriptive statistics

Standard deviation504.66987
Coefficient of variation (CV)0.37677376
Kurtosis-1.0942533
Mean1339.4507
Median Absolute Deviation (MAD)422
Skewness0.078501329
Sum7.7321398 × 108
Variance254691.68
MonotonicityNot monotonic
2024-03-30T03:11:12.405503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 11652
 
2.0%
700 8599
 
1.5%
800 5189
 
0.9%
630 3964
 
0.7%
900 3437
 
0.6%
615 3404
 
0.6%
1000 3281
 
0.6%
830 3199
 
0.6%
730 3041
 
0.5%
715 2863
 
0.5%
Other values (1232) 528633
91.6%
ValueCountFrequency (%)
1 31
< 0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 36
< 0.1%
10 21
< 0.1%
11 1
 
< 0.1%
12 3
 
< 0.1%
ValueCountFrequency (%)
2359 911
0.2%
2358 91
 
< 0.1%
2357 59
 
< 0.1%
2356 101
 
< 0.1%
2355 236
 
< 0.1%
2354 47
 
< 0.1%
2353 123
 
< 0.1%
2352 28
 
< 0.1%
2351 26
 
< 0.1%
2350 214
 
< 0.1%

DEP_TIME
Real number (ℝ)

MISSING 

Distinct1427
Distinct (%)0.3%
Missing11603
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean1339.6753
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:12.674719image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile557
Q1906
median1327
Q31801
95-th percentile2204.1
Maximum2400
Range2399
Interquartile range (IQR)895

Descriptive statistics

Standard deviation527.69883
Coefficient of variation (CV)0.39390054
Kurtosis-0.9713539
Mean1339.6753
Median Absolute Deviation (MAD)428
Skewness0.015188589
Sum7.577994 × 108
Variance278466.05
MonotonicityNot monotonic
2024-03-30T03:11:13.020490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1424
 
0.2%
556 1343
 
0.2%
557 1336
 
0.2%
558 1298
 
0.2%
559 1189
 
0.2%
655 1121
 
0.2%
554 1110
 
0.2%
600 1099
 
0.2%
656 1031
 
0.2%
659 1006
 
0.2%
Other values (1417) 553702
95.9%
(Missing) 11603
 
2.0%
ValueCountFrequency (%)
1 141
< 0.1%
2 99
< 0.1%
3 109
< 0.1%
4 90
< 0.1%
5 99
< 0.1%
6 98
< 0.1%
7 80
< 0.1%
8 86
< 0.1%
9 85
< 0.1%
10 73
< 0.1%
ValueCountFrequency (%)
2400 92
< 0.1%
2359 143
< 0.1%
2358 134
< 0.1%
2357 173
< 0.1%
2356 154
< 0.1%
2355 163
< 0.1%
2354 151
< 0.1%
2353 165
< 0.1%
2352 154
< 0.1%
2351 155
< 0.1%

DEP_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1277
Distinct (%)0.2%
Missing11607
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean18.912882
Minimum-48
Maximum3695
Zeros26175
Zeros (%)4.5%
Negative281729
Negative (%)48.8%
Memory size4.4 MiB
2024-03-30T03:11:13.367008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-48
5-th percentile-9
Q1-5
median0
Q317
95-th percentile106
Maximum3695
Range3743
Interquartile range (IQR)22

Descriptive statistics

Standard deviation66.348798
Coefficient of variation (CV)3.5081274
Kurtosis179.85788
Mean18.912882
Median Absolute Deviation (MAD)6
Skewness9.9427138
Sum10698166
Variance4402.163
MonotonicityNot monotonic
2024-03-30T03:11:13.727873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 37093
 
6.4%
-4 36143
 
6.3%
-3 35781
 
6.2%
-2 33022
 
5.7%
-1 30495
 
5.3%
-6 28221
 
4.9%
0 26175
 
4.5%
-7 23295
 
4.0%
-8 18119
 
3.1%
-9 12948
 
2.2%
Other values (1267) 284363
49.3%
ValueCountFrequency (%)
-48 1
 
< 0.1%
-40 1
 
< 0.1%
-35 1
 
< 0.1%
-34 2
 
< 0.1%
-33 2
 
< 0.1%
-31 3
 
< 0.1%
-30 7
< 0.1%
-29 9
< 0.1%
-28 15
< 0.1%
-27 16
< 0.1%
ValueCountFrequency (%)
3695 1
< 0.1%
3011 1
< 0.1%
2940 1
< 0.1%
2869 1
< 0.1%
2856 1
< 0.1%
2675 1
< 0.1%
2479 1
< 0.1%
2366 1
< 0.1%
2276 1
< 0.1%
2165 1
< 0.1%

TAXI_OUT
Real number (ℝ)

MISSING 

Distinct176
Distinct (%)< 0.1%
Missing12137
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean17.43053
Minimum1
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:14.121937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median15
Q320
95-th percentile33
Maximum176
Range175
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.105703
Coefficient of variation (CV)0.57977026
Kurtosis34.686263
Mean17.43053
Median Absolute Deviation (MAD)4
Skewness4.3880348
Sum9850428
Variance102.12523
MonotonicityNot monotonic
2024-03-30T03:11:14.643626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 46676
 
8.1%
13 46610
 
8.1%
14 43911
 
7.6%
11 43232
 
7.5%
15 39598
 
6.9%
10 35153
 
6.1%
16 35060
 
6.1%
17 30368
 
5.3%
18 26135
 
4.5%
9 24201
 
4.2%
Other values (166) 194181
33.6%
ValueCountFrequency (%)
1 11
 
< 0.1%
2 18
 
< 0.1%
3 48
 
< 0.1%
4 202
 
< 0.1%
5 585
 
0.1%
6 2449
 
0.4%
7 6649
 
1.2%
8 13912
 
2.4%
9 24201
4.2%
10 35153
6.1%
ValueCountFrequency (%)
176 1
 
< 0.1%
175 1
 
< 0.1%
174 2
< 0.1%
173 1
 
< 0.1%
172 1
 
< 0.1%
171 2
< 0.1%
170 1
 
< 0.1%
169 4
< 0.1%
168 1
 
< 0.1%
167 3
< 0.1%

TAXI_IN
Real number (ℝ)

MISSING 

Distinct165
Distinct (%)< 0.1%
Missing12413
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean8.4008169
Minimum1
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:15.071289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q310
95-th percentile20
Maximum197
Range196
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.3927416
Coefficient of variation (CV)0.88000271
Kurtosis51.096912
Mean8.4008169
Median Absolute Deviation (MAD)2
Skewness5.1665243
Sum4745193
Variance54.652628
MonotonicityNot monotonic
2024-03-30T03:11:15.411451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 82134
14.2%
5 80872
14.0%
6 67273
11.7%
7 54781
9.5%
3 47728
8.3%
8 41116
7.1%
9 31910
 
5.5%
10 25387
 
4.4%
11 19872
 
3.4%
12 15818
 
2.7%
Other values (155) 97958
17.0%
ValueCountFrequency (%)
1 905
 
0.2%
2 12090
 
2.1%
3 47728
8.3%
4 82134
14.2%
5 80872
14.0%
6 67273
11.7%
7 54781
9.5%
8 41116
7.1%
9 31910
 
5.5%
10 25387
 
4.4%
ValueCountFrequency (%)
197 1
 
< 0.1%
192 1
 
< 0.1%
191 1
 
< 0.1%
179 1
 
< 0.1%
175 1
 
< 0.1%
174 1
 
< 0.1%
166 3
< 0.1%
165 2
< 0.1%
164 1
 
< 0.1%
163 2
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

Distinct1317
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1477.6757
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:15.783976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile658
Q11047
median1510
Q31929
95-th percentile2302
Maximum2400
Range2399
Interquartile range (IQR)882

Descriptive statistics

Standard deviation539.7102
Coefficient of variation (CV)0.36524265
Kurtosis-0.48083322
Mean1477.6757
Median Absolute Deviation (MAD)429
Skewness-0.31755571
Sum8.5300605 × 108
Variance291287.1
MonotonicityNot monotonic
2024-03-30T03:11:16.183397image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 3284
 
0.6%
2100 1782
 
0.3%
1200 1700
 
0.3%
1710 1674
 
0.3%
2200 1670
 
0.3%
1915 1647
 
0.3%
1845 1646
 
0.3%
1810 1632
 
0.3%
950 1630
 
0.3%
920 1566
 
0.3%
Other values (1307) 559031
96.8%
ValueCountFrequency (%)
1 82
 
< 0.1%
2 126
 
< 0.1%
3 165
 
< 0.1%
4 111
 
< 0.1%
5 640
0.1%
6 129
 
< 0.1%
7 94
 
< 0.1%
8 108
 
< 0.1%
9 52
 
< 0.1%
10 705
0.1%
ValueCountFrequency (%)
2400 1
 
< 0.1%
2359 3284
0.6%
2358 718
 
0.1%
2357 710
 
0.1%
2356 759
 
0.1%
2355 1111
 
0.2%
2354 449
 
0.1%
2353 534
 
0.1%
2352 510
 
0.1%
2351 489
 
0.1%

ARR_TIME
Real number (ℝ)

MISSING 

Distinct1440
Distinct (%)0.3%
Missing12413
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean1431.7519
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:16.509832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile414
Q11019
median1444
Q31918
95-th percentile2256
Maximum2400
Range2399
Interquartile range (IQR)899

Descriptive statistics

Standard deviation576.26535
Coefficient of variation (CV)0.40248968
Kurtosis-0.42504023
Mean1431.7519
Median Absolute Deviation (MAD)444
Skewness-0.39595414
Sum8.0872361 × 108
Variance332081.75
MonotonicityNot monotonic
2024-03-30T03:11:16.850219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1139 635
 
0.1%
1138 635
 
0.1%
913 633
 
0.1%
1146 625
 
0.1%
1636 619
 
0.1%
1148 614
 
0.1%
1903 611
 
0.1%
1134 609
 
0.1%
932 607
 
0.1%
1128 607
 
0.1%
Other values (1430) 558654
96.8%
(Missing) 12413
 
2.2%
ValueCountFrequency (%)
1 468
0.1%
2 395
0.1%
3 398
0.1%
4 371
0.1%
5 369
0.1%
6 365
0.1%
7 398
0.1%
8 403
0.1%
9 378
0.1%
10 335
0.1%
ValueCountFrequency (%)
2400 365
0.1%
2359 430
0.1%
2358 414
0.1%
2357 458
0.1%
2356 453
0.1%
2355 452
0.1%
2354 443
0.1%
2353 435
0.1%
2352 473
0.1%
2351 500
0.1%

ARR_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1303
Distinct (%)0.2%
Missing14458
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean14.405798
Minimum-119
Maximum3680
Zeros10607
Zeros (%)1.8%
Negative303542
Negative (%)52.6%
Memory size4.4 MiB
2024-03-30T03:11:17.205332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-119
5-th percentile-25
Q1-13
median-2
Q317
95-th percentile106
Maximum3680
Range3799
Interquartile range (IQR)30

Descriptive statistics

Standard deviation68.020192
Coefficient of variation (CV)4.7217232
Kurtosis163.4349
Mean14.405798
Median Absolute Deviation (MAD)13
Skewness9.2428885
Sum8107641
Variance4626.7465
MonotonicityNot monotonic
2024-03-30T03:11:17.539527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9 14573
 
2.5%
-11 14385
 
2.5%
-10 14315
 
2.5%
-8 14282
 
2.5%
-7 14183
 
2.5%
-12 14004
 
2.4%
-13 13782
 
2.4%
-6 13738
 
2.4%
-5 13209
 
2.3%
-14 13199
 
2.3%
Other values (1293) 423134
73.3%
(Missing) 14458
 
2.5%
ValueCountFrequency (%)
-119 1
 
< 0.1%
-92 1
 
< 0.1%
-82 4
< 0.1%
-81 2
 
< 0.1%
-78 2
 
< 0.1%
-77 4
< 0.1%
-76 7
< 0.1%
-75 5
< 0.1%
-74 8
< 0.1%
-73 3
 
< 0.1%
ValueCountFrequency (%)
3680 1
< 0.1%
3045 1
< 0.1%
2941 1
< 0.1%
2913 1
< 0.1%
2856 1
< 0.1%
2679 1
< 0.1%
2485 1
< 0.1%
2376 1
< 0.1%
2290 1
< 0.1%
2160 1
< 0.1%

CANCELLED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0.0
565043 
1.0
 
12219

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1731786
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 565043
97.9%
1.0 12219
 
2.1%

Length

2024-03-30T03:11:17.843860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:11:18.098783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 565043
97.9%
1.0 12219
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1142305
66.0%
. 577262
33.3%
1 12219
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1731786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1142305
66.0%
. 577262
33.3%
1 12219
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1731786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1142305
66.0%
. 577262
33.3%
1 12219
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1731786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1142305
66.0%
. 577262
33.3%
1 12219
 
0.7%

CANCELLATION_CODE
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing565043
Missing (%)97.9%
Memory size4.4 MiB
B
5223 
A
4669 
C
2314 
D
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12219
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
B 5223
 
0.9%
A 4669
 
0.8%
C 2314
 
0.4%
D 13
 
< 0.1%
(Missing) 565043
97.9%

Length

2024-03-30T03:11:18.351552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:11:18.588469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
b 5223
42.7%
a 4669
38.2%
c 2314
18.9%
d 13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
B 5223
42.7%
A 4669
38.2%
C 2314
18.9%
D 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12219
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 5223
42.7%
A 4669
38.2%
C 2314
18.9%
D 13
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12219
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 5223
42.7%
A 4669
38.2%
C 2314
18.9%
D 13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12219
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 5223
42.7%
A 4669
38.2%
C 2314
18.9%
D 13
 
0.1%

DIVERTED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0.0
575023 
1.0
 
2239

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1731786
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 575023
99.6%
1.0 2239
 
0.4%

Length

2024-03-30T03:11:18.843785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:11:19.061123image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 575023
99.6%
1.0 2239
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1152285
66.5%
. 577262
33.3%
1 2239
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1731786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1152285
66.5%
. 577262
33.3%
1 2239
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1731786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1152285
66.5%
. 577262
33.3%
1 2239
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1731786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1152285
66.5%
. 577262
33.3%
1 2239
 
0.1%

AIR_TIME
Real number (ℝ)

MISSING 

Distinct599
Distinct (%)0.1%
Missing14458
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean114.82299
Minimum8
Maximum645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:19.327391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile35
Q163
median97
Q3144
95-th percentile275
Maximum645
Range637
Interquartile range (IQR)81

Descriptive statistics

Standard deviation70.987022
Coefficient of variation (CV)0.61823006
Kurtosis2.0979689
Mean114.82299
Median Absolute Deviation (MAD)39
Skewness1.3653817
Sum64622836
Variance5039.1573
MonotonicityNot monotonic
2024-03-30T03:11:19.780556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 4835
 
0.8%
64 4791
 
0.8%
63 4765
 
0.8%
66 4707
 
0.8%
61 4693
 
0.8%
65 4679
 
0.8%
60 4586
 
0.8%
54 4586
 
0.8%
53 4538
 
0.8%
43 4538
 
0.8%
Other values (589) 516086
89.4%
(Missing) 14458
 
2.5%
ValueCountFrequency (%)
8 7
 
< 0.1%
9 15
 
< 0.1%
10 21
 
< 0.1%
11 11
 
< 0.1%
12 13
 
< 0.1%
13 21
 
< 0.1%
14 32
 
< 0.1%
15 38
 
< 0.1%
16 97
< 0.1%
17 223
< 0.1%
ValueCountFrequency (%)
645 1
 
< 0.1%
627 1
 
< 0.1%
625 1
 
< 0.1%
624 1
 
< 0.1%
622 1
 
< 0.1%
620 3
< 0.1%
619 2
< 0.1%
618 1
 
< 0.1%
617 1
 
< 0.1%
616 1
 
< 0.1%

CARRIER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1015
Distinct (%)0.7%
Missing424994
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean26.337241
Minimum0
Maximum3045
Zeros64488
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:20.153707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q323
95-th percentile109
Maximum3045
Range3045
Interquartile range (IQR)23

Descriptive statistics

Standard deviation80.019964
Coefficient of variation (CV)3.038282
Kurtosis161.56186
Mean26.337241
Median Absolute Deviation (MAD)5
Skewness9.9898461
Sum4010319
Variance6403.1947
MonotonicityNot monotonic
2024-03-30T03:11:20.502503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64488
 
11.2%
1 2952
 
0.5%
2 2930
 
0.5%
4 2814
 
0.5%
3 2799
 
0.5%
6 2786
 
0.5%
7 2602
 
0.5%
5 2592
 
0.4%
15 2504
 
0.4%
8 2484
 
0.4%
Other values (1005) 63317
 
11.0%
(Missing) 424994
73.6%
ValueCountFrequency (%)
0 64488
11.2%
1 2952
 
0.5%
2 2930
 
0.5%
3 2799
 
0.5%
4 2814
 
0.5%
5 2592
 
0.4%
6 2786
 
0.5%
7 2602
 
0.5%
8 2484
 
0.4%
9 2358
 
0.4%
ValueCountFrequency (%)
3045 1
< 0.1%
2940 1
< 0.1%
2869 1
< 0.1%
2856 1
< 0.1%
2675 1
< 0.1%
2479 1
< 0.1%
2276 1
< 0.1%
2160 1
< 0.1%
1996 1
< 0.1%
1970 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct479
Distinct (%)0.3%
Missing424994
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean4.1409817
Minimum0
Maximum1489
Zeros141926
Zeros (%)24.6%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:20.825101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15
Maximum1489
Range1489
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.571241
Coefficient of variation (CV)7.382607
Kurtosis553.68909
Mean4.1409817
Median Absolute Deviation (MAD)0
Skewness19.479841
Sum630539
Variance934.60075
MonotonicityNot monotonic
2024-03-30T03:11:21.155694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 141926
 
24.6%
10 227
 
< 0.1%
6 220
 
< 0.1%
2 203
 
< 0.1%
9 192
 
< 0.1%
1 190
 
< 0.1%
7 188
 
< 0.1%
8 186
 
< 0.1%
20 185
 
< 0.1%
4 185
 
< 0.1%
Other values (469) 8566
 
1.5%
(Missing) 424994
73.6%
ValueCountFrequency (%)
0 141926
24.6%
1 190
 
< 0.1%
2 203
 
< 0.1%
3 175
 
< 0.1%
4 185
 
< 0.1%
5 184
 
< 0.1%
6 220
 
< 0.1%
7 188
 
< 0.1%
8 186
 
< 0.1%
9 192
 
< 0.1%
ValueCountFrequency (%)
1489 1
< 0.1%
1429 1
< 0.1%
1239 1
< 0.1%
1197 1
< 0.1%
1159 1
< 0.1%
1150 1
< 0.1%
1130 1
< 0.1%
1105 1
< 0.1%
1086 1
< 0.1%
1085 1
< 0.1%

NAS_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct450
Distinct (%)0.3%
Missing424994
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean13.432008
Minimum0
Maximum1708
Zeros79641
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:21.504528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316
95-th percentile60
Maximum1708
Range1708
Interquartile range (IQR)16

Descriptive statistics

Standard deviation34.309573
Coefficient of variation (CV)2.5543145
Kurtosis280.10376
Mean13.432008
Median Absolute Deviation (MAD)0
Skewness11.36759
Sum2045265
Variance1177.1468
MonotonicityNot monotonic
2024-03-30T03:11:21.872573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79641
 
13.8%
1 4169
 
0.7%
2 2951
 
0.5%
3 2740
 
0.5%
4 2555
 
0.4%
5 2444
 
0.4%
15 2412
 
0.4%
16 2285
 
0.4%
6 2236
 
0.4%
7 2165
 
0.4%
Other values (440) 48670
 
8.4%
(Missing) 424994
73.6%
ValueCountFrequency (%)
0 79641
13.8%
1 4169
 
0.7%
2 2951
 
0.5%
3 2740
 
0.5%
4 2555
 
0.4%
5 2444
 
0.4%
6 2236
 
0.4%
7 2165
 
0.4%
8 1968
 
0.3%
9 2025
 
0.4%
ValueCountFrequency (%)
1708 1
< 0.1%
1372 1
< 0.1%
1344 1
< 0.1%
1306 1
< 0.1%
1252 1
< 0.1%
1214 1
< 0.1%
1190 1
< 0.1%
1144 1
< 0.1%
1129 1
< 0.1%
1120 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct107
Distinct (%)0.1%
Missing424994
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean0.14624215
Minimum0
Maximum309
Zeros151369
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:22.252303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum309
Range309
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9493141
Coefficient of variation (CV)20.167333
Kurtosis2440.9965
Mean0.14624215
Median Absolute Deviation (MAD)0
Skewness40.745696
Sum22268
Variance8.6984539
MonotonicityNot monotonic
2024-03-30T03:11:22.580931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 151369
 
26.2%
16 37
 
< 0.1%
14 36
 
< 0.1%
12 33
 
< 0.1%
21 33
 
< 0.1%
8 33
 
< 0.1%
10 32
 
< 0.1%
7 31
 
< 0.1%
6 30
 
< 0.1%
17 30
 
< 0.1%
Other values (97) 604
 
0.1%
(Missing) 424994
73.6%
ValueCountFrequency (%)
0 151369
26.2%
1 22
 
< 0.1%
2 15
 
< 0.1%
3 21
 
< 0.1%
4 17
 
< 0.1%
5 27
 
< 0.1%
6 30
 
< 0.1%
7 31
 
< 0.1%
8 33
 
< 0.1%
9 26
 
< 0.1%
ValueCountFrequency (%)
309 1
< 0.1%
259 1
< 0.1%
205 1
< 0.1%
192 1
< 0.1%
168 2
< 0.1%
164 1
< 0.1%
158 1
< 0.1%
155 1
< 0.1%
151 1
< 0.1%
149 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct799
Distinct (%)0.5%
Missing424994
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean31.211791
Minimum0
Maximum3581
Zeros68409
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:11:22.889712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q337
95-th percentile135
Maximum3581
Range3581
Interquartile range (IQR)37

Descriptive statistics

Standard deviation67.132402
Coefficient of variation (CV)2.1508667
Kurtosis142.55055
Mean31.211791
Median Absolute Deviation (MAD)7
Skewness7.9230392
Sum4752557
Variance4506.7593
MonotonicityNot monotonic
2024-03-30T03:11:23.223160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68409
 
11.9%
15 1983
 
0.3%
16 1810
 
0.3%
17 1719
 
0.3%
18 1691
 
0.3%
19 1604
 
0.3%
20 1540
 
0.3%
21 1500
 
0.3%
14 1459
 
0.3%
22 1393
 
0.2%
Other values (789) 69160
 
12.0%
(Missing) 424994
73.6%
ValueCountFrequency (%)
0 68409
11.9%
1 1054
 
0.2%
2 1074
 
0.2%
3 1068
 
0.2%
4 1128
 
0.2%
5 1155
 
0.2%
6 1292
 
0.2%
7 1288
 
0.2%
8 1243
 
0.2%
9 1228
 
0.2%
ValueCountFrequency (%)
3581 1
< 0.1%
2366 1
< 0.1%
1787 1
< 0.1%
1734 1
< 0.1%
1547 1
< 0.1%
1514 1
< 0.1%
1513 1
< 0.1%
1451 1
< 0.1%
1450 1
< 0.1%
1437 1
< 0.1%

Interactions

2024-03-30T03:10:41.075215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:32.298839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:39.847986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:46.577501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:54.356599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:01.036700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:08.145414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:15.020409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:21.719345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:28.635332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:35.064582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:41.890279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:48.731811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:56.073664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:02.809996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:10.033771image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:16.773168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:22.711456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:29.035200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:34.801292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:41.406049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:32.865287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:40.205436image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:46.931477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:54.746926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:01.365010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:08.488307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:15.455389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:22.092883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:28.965948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:35.381154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:42.311930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:49.066901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:56.429661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:03.186982image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:10.399327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:17.052092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:23.028527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:29.442255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:35.089961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:41.700916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:33.187333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:40.519840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:47.262776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:55.095849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:01.678562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:08.829806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:15.797585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:22.420884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:29.281008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:35.704819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:42.647496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:49.380120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:56.757514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:03.577062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:10.715627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:17.315102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:23.337603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:29.707660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:35.354582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:42.024326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:33.527257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:40.870170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:47.621918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:55.468386image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:02.042547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:09.192933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:16.187882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:22.772030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:29.654564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:36.104042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:42.995680image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:49.717993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:57.124075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:03.974822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:11.063591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:17.606416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:23.675741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:30.007208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:35.629811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:42.313948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:33.951820image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:41.186006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:47.961628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:55.821535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:02.376537image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:09.482465image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:16.502949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:23.103121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:29.995892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:36.440095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:43.321359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:50.013139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:57.464101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:04.388887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:11.371852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:17.847583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:23.987635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:30.312175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:35.882383image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:42.611497image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:34.338150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:41.541251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:48.358328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:56.230329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:02.718902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:09.805455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:16.862359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:23.483521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:30.354203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:36.777511image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:43.673865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:50.407451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:57.811949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:04.790688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:11.898286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:18.188784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:24.346412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:30.600287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:36.188482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:42.866580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:34.714706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:41.840552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:48.705173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:56.559892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:03.043057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:10.173203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:17.169708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:23.786862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:30.670492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:37.115077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:44.041673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:50.754832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:58.178462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:05.169168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:12.257213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:18.442372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:24.618463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:30.888564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:36.464707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:43.127170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:35.078088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:42.206375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:49.039724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:56.873037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:03.343359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:10.486752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:17.472740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:24.275141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:30.973275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:37.443570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:44.421381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:51.273246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:58.506448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:05.559373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:12.591120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:18.654845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:24.869310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:31.126304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:36.888729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:43.404579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:35.449979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:42.520198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:49.381190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:57.185384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:03.809260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:10.764712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:09:21.052351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:28.006231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:34.479119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:41.114754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:48.063818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:55.407879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:02.209468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:09.336252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:16.228015image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:22.051384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:28.080534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:34.254062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:40.440692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:46.735930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:39.475723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:46.211928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:08:53.742049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:00.668036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:07.737358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:14.501843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:21.312805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:28.277767image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:34.705898image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:41.382772image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:48.350244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:09:55.684894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:02.462879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:09.632925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:16.488326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:22.386915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:28.751530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:34.523406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:10:40.744065image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-30T03:10:47.473128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T03:10:50.313978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
016/5/2023 12:00:00 AM9E490111057CLTCharlotte, NCNorth Carolina3612478JFKNew York, NYNew York2219421944.02.027.014.022002151.0-9.00.0NaN0.086.0NaNNaNNaNNaNNaN
116/5/2023 12:00:00 AM9E490112478JFKNew York, NYNew York2211057CLTCharlotte, NCNorth Carolina3616261620.0-6.024.011.018501818.0-32.00.0NaN0.083.0NaNNaNNaNNaNNaN
216/5/2023 12:00:00 AM9E490310599BHMBirmingham, ALAlabama5112953LGANew York, NYNew York2211251123.0-2.015.06.014581444.0-14.00.0NaN0.0120.0NaNNaNNaNNaNNaN
316/5/2023 12:00:00 AM9E490312953LGANew York, NYNew York2210599BHMBirmingham, ALAlabama51850846.0-4.037.03.010391024.0-15.00.0NaN0.0118.0NaNNaNNaNNaNNaN
416/5/2023 12:00:00 AM9E490410581BGRBangor, MEMaine1212953LGANew York, NYNew York2212181215.0-3.010.06.013591337.0-22.00.0NaN0.066.0NaNNaNNaNNaNNaN
516/5/2023 12:00:00 AM9E490412953LGANew York, NYNew York2210581BGRBangor, MEMaine12945941.0-4.045.04.011331133.00.00.0NaN0.063.0NaNNaNNaNNaNNaN
616/5/2023 12:00:00 AM9E490512953LGANew York, NYNew York2211267DAYDayton, OHOhio4420292025.0-4.039.06.022372232.0-5.00.0NaN0.082.0NaNNaNNaNNaNNaN
716/5/2023 12:00:00 AM9E490610397ATLAtlanta, GAGeorgia3413377MLUMonroe, LALouisiana7210101022.012.044.02.010481121.033.00.0NaN0.073.012.00.021.00.00.0
816/5/2023 12:00:00 AM9E490613377MLUMonroe, LALouisiana7210397ATLAtlanta, GAGeorgia3411331200.027.09.010.014191430.011.00.0NaN0.071.0NaNNaNNaNNaNNaN
916/5/2023 12:00:00 AM9E490710581BGRBangor, MEMaine1212478JFKNew York, NYNew York2219151906.0-9.017.038.020562104.08.00.0NaN0.063.0NaNNaNNaNNaNNaN
DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
57725276/25/2023 12:00:00 AMYX582610721BOSBoston, MAMassachusetts1312953LGANew York, NYNew York2219002003.063.087.011.020432222.099.00.0NaN0.041.00.063.036.00.00.0
57725376/25/2023 12:00:00 AMYX582711278DCAWashington, DCVirginia3810721BOSBoston, MAMassachusetts13900852.0-8.017.09.010521027.0-25.00.0NaN0.069.0NaNNaNNaNNaNNaN
57725476/25/2023 12:00:00 AMYX582911042CLECleveland, OHOhio4410721BOSBoston, MAMassachusetts13610627.017.010.09.0805810.05.00.0NaN0.084.0NaNNaNNaNNaNNaN
57725576/25/2023 12:00:00 AMYX583312478JFKNew York, NYNew York2211066CMHColumbus, OHOhio4421302201.031.014.017.023442343.0-1.00.0NaN0.071.0NaNNaNNaNNaNNaN
57725676/25/2023 12:00:00 AMYX583412953LGANew York, NYNew York2213485MSNMadison, WIWisconsin4520352250.0135.031.06.0222417.0113.00.0NaN0.0110.049.00.00.00.064.0
57725776/25/2023 12:00:00 AMYX583910721BOSBoston, MAMassachusetts1311066CMHColumbus, OHOhio4413351331.0-4.016.09.015501529.0-21.00.0NaN0.093.0NaNNaNNaNNaNNaN
57725876/25/2023 12:00:00 AMYX583911066CMHColumbus, OHOhio4410721BOSBoston, MAMassachusetts1317001714.014.048.010.019111946.035.00.0NaN0.094.00.00.035.00.00.0
57725976/25/2023 12:00:00 AMYX584412953LGANew York, NYNew York2210721BOSBoston, MAMassachusetts1311001056.0-4.023.08.012301209.0-21.00.0NaN0.042.0NaNNaNNaNNaNNaN
57726076/25/2023 12:00:00 AMYX584510154ACKNantucket, MAMassachusetts1312478JFKNew York, NYNew York2211401647.0307.022.018.013041805.0301.00.0NaN0.038.00.0301.00.00.00.0
57726176/25/2023 12:00:00 AMYX584611066CMHColumbus, OHOhio4412478JFKNew York, NYNew York2211521157.05.014.011.013591351.0-8.00.0NaN0.089.0NaNNaNNaNNaNNaN